Whilst computerisation has clearly been beneficial to numerous aspects of modern life, its increasing use in data capture has given rise to a new problem. Automated monitors and measuring systems can take readings more frequently than was possible with their manually operated predecessors. Whenever a decision or assessment is to be made based on the readings from these systems, there is accordingly a vast amount of data available. The sheer volume of data alone may obscure judgment but, more often than not, the assessment must also be made rapidly. Such situations will be referred to as data intensive environments.
One example of a data intensive environment is that encountered in a hospital, in particular in intensive care and high dependency units. Current clinical practice requires that a plethora of specific medical data are measured in the critically ill patient. Different data are collected at various intervals and are traditionally entered onto large paper-based observation charts. Psychological studies have shown that the normal human brain can handle some 20 variables at any one time. There is thus always a danger of information overload, even for skilled staff. Each patient's paper chart must be studied carefully in order to detect important changes in his or her condition. Unfortunately, there is often only recognition of an acute system failure once an adverse trend has been established.
There is now the added complication that newer technological advances have allowed closer monitoring of a patient, for example heart rate and blood pressure changes may now be recorded every few seconds; this has also increased the contribution to the data burden. Although theoretically therefore, such close monitoring may allow early detection of adverse trends so that prompt early corrective measures may be instituted, in practice the amount of data often makes its interpretation more difficult.
The clinical environment is rife with distractions which often put staff under extreme pressure; this is particularly exacerbated with the high level of alarms. Moreover, the level of expertise amongst clinicians for signal interpretation can vary considerably, with the lesser skilled staff more likely to make errors in diagnosis and selection of the most appropriate treatment. Particularly where it is necessary for staff to assess medical data by referring to known organ system failure scoring systems, lesser skilled staff are more likely to make an inaccurate assessment of the patient's condition, or to take longer to recognise an adverse trend.
A further problem is that the data available needs to be assessed having regard to recent clinical interventions, which interventions have traditionally only been recorded by nursing staff as hand annotations to paper charts.
U.S. Pat. No. 5,921,920 to Marshall et al. describes a patient monitoring system, which creates graphical displays of pulmonary and other patient functions in order better to present a wealth of information to the clinician. In a preferred display, eight principal variables are displayed radially, with their arrangement and size intended to assist the clinician. This prior art system, although capable of providing a sophisticated modelling capacity, does not display the overall patient status in such a way as to be readily intelligible by lesser skilled staff (or indeed, relatives of patients); nor does it display interventions or facilitate an assessment of their effect on related organ systems.
There is therefore a perceived need to provide a system which assists an assessor in making a judgment based on analysis of large amounts of data, by enabling an increase in the speed with which the judgment is reached and potentially improving the accuracy of the diagnosis. In particular, in the field of critically ill patient care, there is a perceived need for a system with which regularly-collected patient data can be distilled to provide a reduced data set from which an assessment of patient condition can more readily be made.